How Stale Is Your AI? Release Dates and Training Cutoffs for 20 Current Models

TL;DR – Model freshness varies widely; many top‑tier models launch months after their training data stops, and only 5 of 8 labs publicly disclose a cutoff date.

What the tracker measures

Conclusion: The "Stale AI" page lists two timestamps for each model – the release date (when the model became publicly available) and the training cutoff (the latest date of data the model was trained on). The gap between them quantifies how out‑of‑date a model’s knowledge is at launch.

The site tracks 20 models from 8 major labs (Meta, Anthropic, Mistral AI, Google DeepMind, OpenAI, Alibaba, xAI, DeepSeek).
Data are refreshed automatically; counters count upward from each date.
A JSON dump (models.json) provides the raw numbers for programmatic use.

Release‑date vs. training‑cutoff gaps

Conclusion: The largest gaps exceed five months, meaning users interact with models that lack half‑year‑old information.

Model	Lab	Released	Training cutoff	Gap (months)
Llama 4	Meta	Apr 5 2025	Aug 2024	~8
Claude Haiku 4.5	Anthropic	Oct 15 2025	Jul 2025	~3
Gemini 3.1 Pro	Google DeepMind	Feb 19 2026	Jan 2025	~13
Claude Sonnet 5	Anthropic	Jun 30 2026	Jan 2026	~5
GPT‑5.6 Sol / Luna	OpenAI	Jul 9 2026	Feb 16 2026	~5
Claude Opus 5	Anthropic	Jul 24 2026	May 2026	~2
Grok 4.6	xAI	Aug 12 2026	Feb 1 2026	~6
GPT‑6 Astra	OpenAI	Sep 3 2026	Apr 30 2026	~4

Models marked “Not established” have no published cutoff, so the gap cannot be calculated.

Labs that publish cutoffs

Conclusion: Only five labs (Anthropic, Google DeepMind, Meta, OpenAI, xAI) provide at least one official cutoff date; the remaining three (Mistral AI, Alibaba, DeepSeek) do not.

10 of the 20 listed models have a disclosed cutoff.
A blank entry does not prove the lab never published a date; it indicates the author could not locate one.

Does web‑search tooling close the gap?

Conclusion: No. Search‑augmented responses use external pages only for the current query and do not update the model’s internal knowledge base.

"When a model searches the web for you it is not learning anything. It reads a few pages, uses them in that one answer, and forgets. Open a new chat and it is April again. Search tools paper over the gap. They never close it."

Therefore, relying on browsing to compensate for stale training data is a temporary fix, not a permanent solution.

Community observations from Hacker News

Conclusion: Practitioners notice the stale‑data problem less often when they combine reasoning, tool use, and web search, but the issue still surfaces in concrete cases.

gjskngnf reported that after prompting a model to verify a claim via Reuters, the model finally accepted the fact, suggesting that explicit source‑checking can overcome stale knowledge.
jasonjmcghee echoed that good reasoning and tool use have reduced the impact of cutoffs.
delichon described a real‑world failure: a model still identified Joe Biden as president months after the 2024 election, only correcting itself when forced to browse the web.
VCFundedGenYer highlighted that older, self‑hosted models (e.g., Ollama containers) still ship with 2023 cutoffs, which is unacceptable for many applications.
asimovDev noted a concrete error where Qwen‑3.8 generated a 2025 copyright footer and assumed an outdated version of the Astro framework.

These anecdotes confirm that stale knowledge can lead to factual errors, especially for time‑sensitive domains like politics, software versions, and copyright dates.

Practical advice for developers

Conclusion: Treat model knowledge as a snapshot and always verify time‑sensitive facts with external sources.

Ask the model its cutoff – a well‑behaved model will either disclose the date or admit uncertainty. Verify the answer against the tracker.
Use explicit web‑search tools for any query about recent events, software releases, or regulatory changes.
Prefer models with published cutoffs when freshness is critical; the disclosed date gives a clear bound on what the model knows.
Monitor model releases – the tracker updates automatically, so you can programmatically alert your team when a newer model with a smaller gap becomes available.

Why freshness matters

Conclusion: Out‑of‑date knowledge can cause misinformation, legal risk, and degraded user trust, especially as AI becomes a primary information source.

A five‑month lag means a model cannot know about major events such as elections, policy changes, or software releases that occurred after its cutoff.
In regulated industries (finance, healthcare), relying on stale data can lead to compliance violations.
User expectations for up‑to‑date answers are rising; a model that repeatedly provides outdated facts will quickly lose credibility.

By tracking release dates and training cutoffs, stakeholders can make informed choices about which models to deploy, when to supplement them with retrieval tools, and when to plan for model upgrades.

Source: The data and explanations are taken directly from the "How Stale Is Your AI?" tracker (https://stale.jock.pl/) and the associated Hacker News discussion (https://news.ycombinator.com/item?id=49726343).